Most prognostic methods require a decent amount of data for model training.
In reality, however, the amount of historical data owned by a single
organization might be small or not large enough to train a reliable prognostic
model. To address this challenge, this article proposes a federated prognostic
model that allows multiple users to jointly construct a failure time prediction
model using their multi-stream, high-dimensional, and incomplete data while
keeping each user's data local and confidential. The prognostic model first
employs multivariate functional principal component analysis to fuse the
multi-stream degradation signals. Then, the fused features coupled with the
times-to-failure are utilized to build a (log)-location-scale regression model
for failure prediction. To estimate parameters using distributed datasets and
keep the data privacy of all participants, we propose a new federated algorithm
for feature extraction. Numerical studies indicate that the performance of the
proposed model is the same as that of classic non-federated prognostic models
and is better than that of the models constructed by each user itself.

本文提出了一种联邦预测模型，允许多个用户共同构建基于多流、高维和不完整数据的故障时间预测模型，同时保持每个用户的数据本地和保密。该预测模型首先使用多元功能主成分分析融合多流退化信号，然后利用融合特征和失效时间来构建（对数）位置 - 比例回归模型进行故障预测。为了使用分布式数据集进行参数估计并保护所有参与者的数据隐私，我们提出了一种新的联邦算法进行特征提取。数值研究表明，所提出的模型的性能与经典的非联邦预测模型相同，并且优于每个用户自建模型的性能。